Monte Carlo go has a way to go

Haruhiro Yoshimoto, Kazuki Yoshizoe, Tomoyuki Kaneko, Akihiro Kishimoto, Kenjiro Taura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Monte Carlo Go is a promising method to improve the performance of computer Go programs. This approach determines the next move to play based on many Monte Carlo samples. This paper examines the relative advantages of additional samples and enhancements for Monte Carlo Go. By parallelizing Monte Carlo Go, we could increase sample sizes by two orders of magnitude. Experimental results obtained in 9 × 9 Go show strong evidence that there are trade-offs among these advantages and performance, indicating a way for Monte Carlo Go to go.

Original languageEnglish
Title of host publicationProceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
Pages1070-1075
Number of pages6
Publication statusPublished - 2006
Externally publishedYes
Event21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06 - Boston, MA, United States
Duration: Jul 16 2006Jul 20 2006

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume2

Other

Other21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
Country/TerritoryUnited States
CityBoston, MA
Period7/16/067/20/06

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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